A comprehensive review of EEG-based brain–computer interface paradigms

Abiri, Reza; Borhani, Soheil; Sellers, Eric W.; Jiang, Yang; Zhao, Xiaopeng

doi:10.1088/1741-2552/aaf12e

Cited by 634 publications

(412 citation statements)

References 251 publications

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“…In non-invasive BCI paradigms, EEG signals are easily collected without brain surgery and commonly used due to their high temporal resolution [4]. The EEG signals have been applied to various types of BCI paradigms such as event-related potential (ERP) [5], movement-related cortical potential (MRCP) [6] and motor imagery (MI) [7]. EEG-based BCI paradigms have been developed for interaction between users and external devices [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Classification of High-Dimensional Motor Imagery Tasks Based on An End-To-End Role Assigned Convolutional Neural Network

Lee

Jeong

Shim

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Electroencephalogram (EEG) motor imagery (MI) paradigm is widely used in non-invasive BCI to obtain encoded signals contained user intention of movement execution. However, EEG has intricate and non-stationary properties resulting in insufficient decoding performance. By imagining numerous movements of a single-arm, decoding performance can be improved without artificial command matching. In this study, we collected intuitive EEG data contained the nine different types of movements of a single-arm from 9 subjects. We propose an end-toend role assigned convolutional neural network (ERA-CNN) which considers discriminative features of each upper limb region by adopting the principle of a hierarchical CNN architecture. The proposed model outperforms previous methods on 3-class, 5-class and two different types of 7-class classification tasks. Hence, we demonstrate the possibility of decoding user intention by using only EEG signals with robust performance using an ERA-CNN.Index Terms-Brain-computer interface (BCI), Electroencephalogram (EEG), Motor imagery, Convolutional Neural Network (CNN)

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Section: Introductionmentioning

confidence: 99%

Classification of High-Dimensional Motor Imagery Tasks Based on An End-To-End Role Assigned Convolutional Neural Network

Lee

Jeong

Shim

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Many successful approaches to BCI have been presented in the literature [2,9,10]. While many approaches focus on subject specific models due to high inter-subject variances present in larger populations, a wide array of studies have shown the viability of subject independent (SI) models [2,7,10–13]. Reducing complexity is an important part of emerging consumer grade EEG devices.…”

Section: Introductionmentioning

confidence: 99%

A Novel Performance Metric for Multiclass Subject Invariant Brain Computer Interfaces with Imbalanced Classes

Sherwood

Lowe

Derakhshani

2020

Preprint

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20 [Finding suitable common feature sets for use in multiclass subject independent brain-21 computer interface (BCI) classifiers is problematic due to characteristically large 22 inter-subject variation of electroencephalographic signatures. We propose a wrapper 23 search method using a one versus the rest discrete output classifier. Obtaining and 24 evaluating the quality of feature sets requires the development of appropriate 25 classifier metrics. A one versus the rest classifier must be evaluated by a scalar 26 performance metric that provides feedback for the feature search algorithm. However, 27 the one versus the rest discrete classifier is prone to settling into degenerate states for 28 difficult discrimination problems. The chance of occurrence of degeneracy increases 29 with the number of classes, number of subjects and imbalance between the number of 30 samples in the majority and minority classes. This paper proposes a scalar Quality 31 (Q)-factor to compensate for classifier degeneracy and to improve the convergence of 32 the wrapper search. The Q-factor, calculated from the ratio of sensitivity to specificity 33 of the confusion matrix, is applied as a penalty to the accuracy (1-error rate). This 34 method is successfully applied to a multiclass subject independent BCI using 10 35 untrained subjects performing 4 motor tasks in conjunction with the Sequential 36 Floating Forward Selection feature search algorithm and Support Vector Machine 37 classifiers.] 3 38 1. Introduction 39 Brain Computer Interface (BCI) is an ensemble of technologies that seek to establish 40 a pathway of communication capable of translating neurologically derived signals, 41 such as imagined human movements, into computer interpretable commands [1-3]. 42 These commands can be used for the purpose of basic user interface, or for 43 controlling external devices such as a robotic arm or prosthetic [1,4-8]. BCI 44 implementation is a pattern recognition problem where signals derived from different 45 brain states are examined in order to select a set of optimally descriptive features 46 which can be most closely attributed to the associated state, typically using supervised 47 learning. 48 Many successful approaches to BCI have been presented in the literature [2,9,10]. 49 While many approaches focus on subject specific models due to high inter-subject 50 variances present in larger populations, a wide array of studies have shown the 51 viability of subject independent (SI) models [2,7,10-13]. Reducing complexity is an 52 important part of emerging consumer grade EEG devices. Viability of an off the shelf 53 subject independent solution relies on methods capable of being deployed for mobile 54 devices and embedded platforms. While the computational capabilities of these 55 devices are rapidly increasing, power requirements play a key role in feasibility of 56 high computational complexity models. As a result, optimized models based on 57 traditional machine learning techniques gain an advantage for BCI applications. 58 Additional...

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“…Instead, electroencephalography (EEG) is a convenient solution given direct measure of neural activity at milliseconds time precision. Additionally, a wireless EEG headset can make Brain-Computer Interface (BCI) at an individual level simpler to use [18]. EEG has been used as a low-cost brain imaging technique for attention evaluation and training by many researchers [19].…”

Section: Introductionmentioning

confidence: 99%

“…EEG has been used as a low-cost brain imaging technique for attention evaluation and training by many researchers [19]. A number of EEG paradigms have been suggested in connection with attention deficit and attention enhancement [18,19]. In 1976, Lubar and Shouse [20] pioneered the EEG-based neurofeedback training for patients with attention disorders.…”

Section: Introductionmentioning

confidence: 99%

Decoding Attentional State to Faces and Scenes Using EEG Brainwaves

et al. 2019

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Attention is the ability to facilitate processing perceptually salient information while blocking the irrelevant information to an ongoing task. For example, visual attention is a complex phenomenon of searching for a target while filtering out competing stimuli. In the present study, we developed a new Brain-Computer Interface (BCI) platform to decode brainwave patterns during sustained attention in a participant. Scalp electroencephalography (EEG) signals using a wireless headset were collected in real time during a visual attention task. In our experimental protocol, we primed participants to discriminate a sequence of composite images. Each image was a fair superimposition of a scene and a face image. The participants were asked to respond to the intended subcategory (e.g., indoor scenes) while withholding their responses for the irrelevant subcategories (e.g., outdoor scenes). We developed an individualized model using machine learning techniques to decode attentional state of the participant based on their brainwaves. Our model revealed the instantaneous attention towards face and scene categories. We conducted the experiment with six volunteer participants. The average decoding accuracy of our model was about 77%, which was comparable with a former study using functional magnetic resonance imaging (fMRI). The present work was an attempt to reveal momentary level of sustained attention using EEG signals. The platform may have potential applications in visual attention evaluation and closed-loop brainwave regulation in future.

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A comprehensive review of EEG-based brain–computer interface paradigms

Cited by 634 publications

References 251 publications

Classification of High-Dimensional Motor Imagery Tasks Based on An End-To-End Role Assigned Convolutional Neural Network

Classification of High-Dimensional Motor Imagery Tasks Based on An End-To-End Role Assigned Convolutional Neural Network

A Novel Performance Metric for Multiclass Subject Invariant Brain Computer Interfaces with Imbalanced Classes

Decoding Attentional State to Faces and Scenes Using EEG Brainwaves

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